Gene expression profiling of gastric cancer by microarray combined with laser capture microdissection

AIM： To examine the gene expression profile of gastric cancer （GC） by combination of laser capture microdissection （LCM） and microarray and to correlate the profiling with histological subtypes. METHODS： Using LCM, pure cancer cells were procured from 45 cancerous tissues. After procurement of about 5 000 cells, total RNA was extracted and the quality of RNA was determined before further amplification and hybridization. One microgram of amplified RNA was converted to cDNA and hybridized to cDNA microarray. RESULTS： Among 45 cases, only 21 were qualified for their RNAs. A total of 62 arrays were performed. These included 42 arrays for cancer （21 cases with dyeswab duplication） and 20 arrays for non-tumorous cells （10 cases with dye-swab duplication） with universal reference. Analyzed data showed 504 genes were differentially expressed and could distinguish cancerous and non-cancerous groups with more than 99% accuracy. Of the 504 genes, trefoil factors 1, 2, and 3 were in the list and their expression patterns were consistent with previous reports. Immunohistochemical staining of trefoil factor 1 was also consistent with the array data. Analyses of the tumor group with these 504 genes showed that there were 3 subgroups of GC that did not correspond to any current classification system, including Lauren＇s dassification. CONCLUSION： By using LCM, linear amplification of RNA, and cDNA microarray, we have identified a panel of genes that have the power to discriminate between GC and non-cancer groups. The new molecular classification and the identified novel genes in gastric carcinogenesis deserve further investigations to elucidate their clinicopathological significance.

Identification of differentially expressed genes of primary spermatocyte against round spermatid isolated from human testis using the laser capture microdissection technique

The method of laser capture microdissection (LCM) combined with suppressive subtractive hybridization (SSH) was developed to isolate specific germ cells from human testis sections and to identify the genes expressed during differentiation and development. In the present study, over 10,000 primary spermatocytes and round spermatid cells were successfully isolated by LCM. Using the cDNAs from primary spermatocytes and round spermatids, SSH cDNAs library of primary spermatocyte-specific was constructed. The average insert size of the cDNA isolated from 75 randomly picked white clones was 500 bp, ranging from 250 bp to 1.7 kb. Using the dot-blot method, a total of 421 clones were examined, resulting in the identification of 390 positive clones emitting strong signals. Partial sequence of cDNAs prepared from each clone was determined with an overall success rate of 84.4%. Genes encoding cytochrome c oxidase II and the rescue factor-humanin were most frequently expressed in primary spermatocytes, suggesting their roles involved in meiosis.

Laser capture microdissection enables cellular and molecular studies of tooth root development

Epithelial-mesenchymal interactions（EMIs） are critical for tooth development.Molecular mechanisms mediating these interactions in root formation is not well understood.Laser capture microdissection（LCM） and subsequent microarray analyses enable large scale in situ molecular and cellular studies of root formation but to date have been hindered by technical challenges of gaining intact histological sections of non-decalcified mineralized teeth or jaws with well-preserved RNA.Here,we describe a new method to overcome this obstacle that permits LCM of dental epithelia,adjacent mesenchyme,odontoblasts and cementoblasts from mouse incisors and molars during root development.Using this method,we obtained RNA samples of high quality and successfully performed microarray analyses.Robust differences in gene expression,as well as genes not previously associated with root formation,were identified.Comparison of gene expression data from microarray with real-time reverse transcriptase polymerase chain reaction（RT-PCR） supported our findings.These genes include known markers of dental epithelia,mesenchyme,cementoblasts and odontoblasts,as well as novel genes such as those in the fibulin family.In conclusion,our new approach in tissue preparation enables LCM collection of intact cells with well-preserved RNA allowing subsequent gene expression analyses using microarray and RT-PCR to define key regulators of tooth root development.

Isolation of cardiac conduction system of rat heart by laser capture microdissection

Objective：To isolate ceils of cardiac conduction system （CCS） with laser capture microdissec tion （LCM） and extract and evaluate quality of small amount of RNA from ceils of CCS. Methods： Cryo star sections were followed by H-E staining. 20 pieces of H-E stained eryostat sections were scraped and its RNA was assessed to insure that RNA didn＇t degrade in dyeing and dehydration process. Ceils of CCS were captured with LCM and quality of small amount of RNA was verified with RT-PCR. Results： Ceils of CCS isolated with LCM had clear morphology after staining. High quality RNA was extracted from LCM samples and scraped tissues; 18S rRNA and 28S rRNA were seen distinctly on gel eleetrophoresis. Low level of small amount of RNA extracted from LCM sample was below the limit of detection on gel eleetrophoresis or ultraviolet speetrophotometer. The housekeeping genes β-aetin and GAPDH were successfully amplified with small amount of RNA. Conclusion ：This study resolves the problem of acquiring material of CCS precisely that hinders gene research of CCS. It is found out that the method is easy and reliable to extract and assess the quality of small amount of RNA from mierodisseeted ceils of CCS.

Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution

Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context,significantly enhancing our understanding of the intricate and multifaceted biological system.With an increasing focus on spatial heterogeneity,there is a growing need for unbiased,spatially resolved omics technologies.Laser capture microdissection(LCM)is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest(ROIs)from heterogeneous tissues,with resolutions ranging from single cells to cell populations.Thus,LCM has been widely used for studying the cellular and molecular mechanisms of diseases.This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research.Key attributes of application cases are also highlighted,such as throughput and spatial resolution.In addition,we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research,disease diagnosis,and targeted therapy from the perspective of high-throughput,multi-omics,and single-cell resolution.

Study of differential proteins in lung adenocarcinoma using laser capture microdissection combined with liquid chip-mass spectrometry technology

Background In recent years the proportion of lung adenocarcinoma （adCA） which occurs in lung cancer patients has increased. Using laser capture microdissection （LCM） combined with liquid chip-mass spectrometry technology, we aimed to screen lung cancer biomarkers by studying the proteins in the tissues of adCA. Methods We used LCM and magnetic bead based weak cation exchange （MB-WCX） to separate and purify the homogeneous adCA cells and normal cells from six cases of fresh adCA and matched normal lung tissues. The proteins were analyzed and identified by matrix assisted laser desorption/ionization time-of-fight mass spectrometry （MALDI-OF-MS）. We screened for the best pattern using a radial basic function neural network algorithm. Results About 2.895x10s and 1.584x10s cells were satisfactorily obtained by LCM from six cases of fresh lung adCA and matched normal lung tissues, respectively. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. Comparing the differentially expressed proteins between the lung adCA and the matched normal lung group, 221 and 239 protein peaks, respectively, were found in the mass-to-charge ration （M/Z） between 800 Da and 10 000 Da. According to ttest, the expression of two protein peaks at 7521.5 M/Zand 5079.3 M/Z had the largest difference between tissues. They were more weakly expressed in the lung adCA compared to the matched normal group. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart. A discriminatory pattern which can separate the lung adCA from the matched normal lung tissue consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by a radial basic function neural network algorithm with a sensitivity of 100% and a specificity of 100%. Conclusions Differential proteins in lung adCA were screened using LCM combined with liquid chip-mass spectrometry technology, and a biomarker model was established. It is possible that this technology is going to become a powerful tool in screening and early diagnosis of lung adCA.